Observation of Attention Mechanism Baseline for PCB Surface Inspection System

Fityanul Akhyar, Ledya Novamizanti, Muhammad Azka Imaddudin, Ikhsanico Henda Pratama, Shandy Ramanda Firmansyach, Ming Ching Chang, Chih Yang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Printed circuit boards (PCBs) are critical for interconnecting various components and allowing them to communicate with each other. It is critical to ensure that there are no small surface defects that can negatively impact PCB production. Therefore, template matching is often used in PCB surface inspection systems. Despite its popularity, this method can be improved because inspecting a PCB with a template is inefficient. Currently, integrating the surface inspection system with the deep learning method is proving to be more effective in solving this problem. This paper examines three popular deep learning object recognition methods in order to determine which one is the most effective in terms of attention. These three models are called Carafe, Empirical Attention, and ResNeSt. The experimental results showed that ResNeSt with split attention networks achieves the greatest accuracy in deep learning PCB surface inspection system with a mean average precision (mAP) of 99.2% and an average recall (AR) of 99.5%. The result of this study would improve the effectiveness of PCB surface inspection in controlling production lines.

Original languageEnglish
Title of host publicationAPWiMob 2022 - Proceedings
Subtitle of host publication2022 IEEE Asia Pacific Conference on Wireless and Mobile
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665474863
DOIs
StatePublished - 2022
Event2022 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2022 - Bandung, Indonesia
Duration: 9 Dec 202210 Dec 2022

Publication series

NameAPWiMob 2022 - Proceedings: 2022 IEEE Asia Pacific Conference on Wireless and Mobile

Conference

Conference2022 IEEE Asia Pacific Conference on Wireless and Mobile, APWiMob 2022
Country/TerritoryIndonesia
CityBandung
Period9/12/2210/12/22

Keywords

  • PCB
  • attention network
  • deep learning
  • defect detection

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